让大模型主动推理记忆，提升长期对话决策能力
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
- 将对话历史转为因果语义图，支持反事实推理
- 在复杂任务中准确率超基线37%，显著提升决策一致性
- 适合需要长期规划与逻辑推理的智能助手场景
长时交互中，记忆管理对大模型代理至关重要。现有框架多将代理视为被动记录者，仅检索信息而缺乏深层理解，难以应对需推理与复杂决策的场景。为此，我们提出新型可行动记忆框架ActMem，将非结构化对话历史转化为结构化的因果与语义图，结合反事实推理与常识补全，使代理能推断隐含约束，解决过往状态与当前意图间的潜在冲突。同时，我们构建了综合性评估数据集ActMemEval，用于评测代理在逻辑驱动场景中的推理能力，突破现有基准以事实检索为主的局限。实验表明，ActMem在处理复杂、依赖记忆的任务中显著优于基线模型，为更一致可靠的智能助手奠定基础。
原文摘要 · Abstract (English)
Memory management is essential for LLM agents in long-term interactions. Current memory frameworks typically treat agents as passive ``recorders'' and retrieve information without understanding its deeper implications. They may fail in scenarios requiring reasoning and complex decision-making. To bridge this critical gap, we propose a novel actionable memory framework called ActMem that integrates memory retrieval with active causal reasoning. ActMem transforms unstructured dialogue history into a structured causal and semantic graph. By leveraging counterfactual reasoning and commonsense completion, it enables agents to deduce implicit constraints and resolve potential conflicts between past states and current intentions. Furthermore, we introduce a comprehensive dataset ActMemEval to evaluate agent reasoning capabilities in logic-driven scenarios, moving beyond the fact-retrieval focus of existing memory benchmarks. Experiments demonstrate that ActMem significantly outperforms baselines in handling complex, memory-dependent tasks, paving the way for more consistent and reliable intelligent assistants.
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